{
 "cells": [
  {
   "attachments": {
    "qlearning_pseu.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 算法流程\n",
    "\n",
    "如图所示，$\\text{Q-learning}$ 算法的核心流程如下：\n",
    "\n",
    "![qlearning_pseu.png](attachment:qlearning_pseu.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义超参数\n",
    "\n",
    "为了便于调整和实验，我们把所有的超参数都定义在一个`Python`类中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Config:\n",
    "    def __init__(self) -> None:\n",
    "        ## 通用参数\n",
    "        self.env_id = \"CliffWalking-v0\" # 环境id\n",
    "        self.n_states = 48 # 状态数\n",
    "        self.n_actions = 4 # 动作数\n",
    "        self.render_mode = None # 渲染模式\n",
    "        self.algo_name = \"Qlearning\" # 算法名称\n",
    "        self.seed = 1 # 随机种子\n",
    "        self.device = \"cuda\" # 训练设备，\"cpu\" or \"cuda\"\n",
    "        self.max_episode = 300 # 最大回合数\n",
    "        self.max_step = 200 # 每个回合的最大步数\n",
    "\n",
    "        ## 算法参数\n",
    "        self.epsilon_start = 0.95 # epsilon 初始值\n",
    "        self.epsilon_end = 0.01 # epsilon 终止值\n",
    "        self.epsilon_decay = 300 # epsilon 衰减率\n",
    "        self.gamma = 0.90 # 奖励折扣因子\n",
    "        self.lr = 0.1 # 学习率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义策略\n",
    "\n",
    "策略决定智能体如何选择动作，即智能体是策略的载体。在实践中，我们用一个类来表示策略，通常包含以下几个部分：\n",
    "\n",
    "1. 模型：表示策略的函数近似器，例如神经网络或查找表。\n",
    "2. 采样动作：表示训练模式下如何根据当前状态选择动作，通常包含探索机制。\n",
    "3. 预测动作：表示测试模式下如何根据当前状态选择动作，由于是用来评估训练好的策略或模型的，一般不包含探索机制。\n",
    "4. 更新策略：表示如何根据经验数据来更新策略或模型的参数。\n",
    "\n",
    "在 $\\text{Q-learning}$ 算法中，模型即动作价值函数 $Q(s, a)$ 是用表格来表示的，可以用一个二维数组或者字典来实现。对于其他部分包括更新策略等，可以参照算法流程中的公式来依次实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import torch\n",
    "from collections import defaultdict\n",
    "\n",
    "class Policy(object):\n",
    "    def __init__(self, cfg: Config):\n",
    "        ''' 初始化\n",
    "        '''\n",
    "        self.n_actions: int = cfg.n_actions # 动作数\n",
    "        self.lr: float = cfg.lr \n",
    "        self.gamma: float = cfg.gamma    \n",
    "        self.epsilon: float = cfg.epsilon_start\n",
    "        self.sample_count = 0  # 采样计数，用于 epsilon 衰减\n",
    "        self.epsilon_start: float = cfg.epsilon_start \n",
    "        self.epsilon_end: float = cfg.epsilon_end\n",
    "        self.epsilon_decay: float = cfg.epsilon_decay\n",
    "        self.Q_table = defaultdict(lambda: np.zeros(self.n_actions)) # 使用默认字典来表示 Q(s,a)，初始值为 0\n",
    "\n",
    "    def sample_action(self, state):\n",
    "        ''' 采样动作\n",
    "        ''' \n",
    "        self.sample_count += 1\n",
    "        # epsilon 值需要衰减，衰减方式可以是线性、指数等，以平衡探索和开发\n",
    "        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \\\n",
    "            math.exp(-1. * self.sample_count / self.epsilon_decay) \n",
    "        if np.random.uniform(0, 1) > self.epsilon:\n",
    "            action = np.argmax(self.Q_table[str(state)]) # 选择具有最大 Q 值的动作\n",
    "        else:\n",
    "            action = np.random.choice(self.n_actions) # 随机选择一个动作\n",
    "        return action\n",
    "    \n",
    "    def predict_action(self, state):\n",
    "        ''' 预测动作\n",
    "        '''\n",
    "        action = np.argmax(self.Q_table[str(state)])\n",
    "        return action\n",
    "    \n",
    "    def update(self, state, action, reward, next_state, done):\n",
    "        ''' 更新策略\n",
    "        '''\n",
    "        Q_estimate = self.Q_table[str(state)][action]  # Q 估计值 Q(s,a)\n",
    "        if done: # TD 更新需考虑终止状态\n",
    "            Q_target = reward  \n",
    "        else:\n",
    "            Q_target = reward + self.gamma * np.max(self.Q_table[str(next_state)]) \n",
    "        # Q_target = reward + self.gamma * np.max(self.Q_table[str(next_state)]) * (1 - int(done))\n",
    "        self.Q_table[str(state)][action] += self.lr * (Q_target - Q_estimate) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义工具函数\n",
    "\n",
    "为了保证实验的可复现性，通常需要固定随机种子。因此，我们定义一个工具函数 `set_seed` 来设置所有相关模块的随机种子。另外，为了更好地观察训练过程中的变化情况，我们定义了一些绘图函数来可视化训练结果。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import os\n",
    "import seaborn as sns; sns.set_theme()\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def set_seed(seed = 0):\n",
    "    ''' 固定随机种子\n",
    "    '''\n",
    "    if seed == 0: # 不设置随机种子\n",
    "        return \n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed) \n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    # config for cudnn\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    torch.backends.cudnn.enabled = False\n",
    "\n",
    "def smooth(data, weight=0.9):  \n",
    "    '''用于平滑曲线\n",
    "    '''\n",
    "    last = data[0] \n",
    "    smoothed = []\n",
    "    for point in data:\n",
    "        smoothed_val = last * weight + (1 - weight) * point  # 计算平滑值\n",
    "        smoothed.append(smoothed_val)                    \n",
    "        last = smoothed_val                                \n",
    "    return smoothed\n",
    "\n",
    "def plot_rewards(rewards, ylabel = \"rewards\", title=\"learning curve\"):\n",
    "    ''' 绘制奖励曲线\n",
    "    '''\n",
    "    sns.set_theme()\n",
    "    plt.figure()  \n",
    "    plt.title(f\"{title}\") # 设置标题\n",
    "    plt.xlim(0, len(rewards)) # x轴范围\n",
    "    plt.xlabel('episodes') # x轴标签\n",
    "    plt.ylabel(ylabel) # y轴标签\n",
    "    plt.plot(rewards, label='original') # 绘制原始奖励曲线\n",
    "    plt.plot(smooth(rewards), label='smoothed') # 绘制平滑后的奖励曲线\n",
    "    plt.legend() # 显示图例\n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义环境\n",
    "\n",
    "我们使用 `gymnasium` 库中的 `CliffWalking-v0` 环境来测试我们的 $\\text{Q-learning}$ 算法。这个环境是一个经典的网格世界任务，智能体需要从起点走到终点，同时避免掉入悬崖。状态空间是离散的，共有 48 个状态，表示网格中的每个位置。动作空间也是离散的，共有 4 个动作，分别表示向上、向下、向左和向右移动。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gymnasium as gym\n",
    "\n",
    "ACTION_MAP = {0: 'Up', 1: 'Right', 2: 'Down', 3: 'Left'}\n",
    "\n",
    "def create_env(cfg: Config):\n",
    "    ''' 创建环境并设置随机种子\n",
    "    '''\n",
    "    env = gym.make(cfg.env_id, render_mode = cfg.render_mode) # 创建环境\n",
    "    n_states = env.observation_space.n\n",
    "    n_actions = env.action_space.n\n",
    "    setattr(cfg, 'n_states', n_states)\n",
    "    setattr(cfg, 'n_actions', n_actions)\n",
    "    print(f\"状态数：{n_states}，动作数：{n_actions}\")\n",
    "    return env"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义训练与测试\n",
    "\n",
    "我们按照 $\\text{Q-learning}$ 算法的流程来实现训练与测试过程。在训练过程中，智能体通过与环境交互来收集经验数据，并根据这些数据来更新动作价值函数 $Q(s, a)$。在测试过程中，智能体使用训练好的动作价值函数来选择最优动作，从而评估其性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "def train(cfg: Config, env, policy: Policy):\n",
    "    ''' 训练\n",
    "    '''\n",
    "    print(\"开始训练！\")\n",
    "    s_t = time.time()\n",
    "    rewards = []  # 记录所有回合的奖励\n",
    "    steps = []  # 记录所有回合的步数\n",
    "    for i_ep in range(cfg.max_episode):\n",
    "        ep_reward = 0  # 单回合总奖励\n",
    "        ep_step = 0\n",
    "        state, info = env.reset(seed = cfg.seed)  # 重置环境并获取初始状态\n",
    "        for _ in range(cfg.max_step):\n",
    "            ep_step += 1\n",
    "            action = policy.sample_action(state)  # 采样动作 \n",
    "            next_state, reward, terminated, truncated , info = env.step(action)  # 更新环境并返回新状态、奖励、终止状态、截断标志和其他信息（使用 OpenAI Gym 的 new_step_api）\n",
    "            done = terminated or truncated\n",
    "            policy.update(state, action, reward, next_state, done)  # 更新策略\n",
    "            state = next_state  # 更新状态 \n",
    "            ep_reward += reward \n",
    "            ep_step += 1\n",
    "            if done:\n",
    "                break\n",
    "        rewards.append(ep_reward)\n",
    "        steps.append(ep_step)\n",
    "        if (i_ep + 1) % 10 == 0:\n",
    "            print(f\"回合：{i_ep+1}/{cfg.max_episode}，奖励：{ep_reward:.2f}, 步数：{ep_step}\")\n",
    "    env.close()\n",
    "    print(f\"完成训练！用时：{time.time()-s_t:.2f} 秒\")\n",
    "    return {'rewards':rewards, 'steps':steps}\n",
    "\n",
    "def test(cfg: Config, env, policy: Policy):\n",
    "    print(\"开始测试！\")\n",
    "    rewards = []  # 记录所有回合的奖励\n",
    "    steps = []\n",
    "    s_t = time.time()\n",
    "    for i_ep in range(cfg.max_episode):\n",
    "        ep_reward = 0  # 一轮的累计奖励 \n",
    "        ep_step = 0\n",
    "        action_sequence = []\n",
    "        obs, info = env.reset(seed = cfg.seed)  # 重置环境并获取初始状态\n",
    "        for _ in range(cfg.max_step):\n",
    "            action = policy.predict_action(obs)  # 预测动作 \n",
    "            next_obs, reward, terminated, truncated , info = env.step(action)\n",
    "            done = terminated or truncated\n",
    "            obs = next_obs  # 更新状态 \n",
    "            ep_reward += reward  # 增加奖励\n",
    "            ep_step += 1\n",
    "            action_sequence.append(ACTION_MAP[action])\n",
    "            if done:\n",
    "                break\n",
    "        steps.append(ep_step)\n",
    "        rewards.append(ep_reward)\n",
    "        print(f\"回合：{i_ep+1}/{cfg.max_episode}，奖励：{ep_reward:.2f}, 步数：{ep_step}, 动作序列：{action_sequence}\")\n",
    "    print(f\"完成测试！用时：{time.time()-s_t:.2f} 秒\")\n",
    "    env.close()\n",
    "    return {'rewards':rewards, 'steps':steps}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开始训练\n",
    "\n",
    "定义好以上各个部分后，可以开始训练 $\\text{Q-learning}$ 智能体了。训练过程中，我们会记录每一轮的奖励和步数，并在训练结束后进行可视化展示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "状态数：48，动作数：4\n",
      "开始训练！\n",
      "回合：10/300，奖励：-125.00, 步数：250\n",
      "回合：20/300，奖励：-145.00, 步数：290\n",
      "回合：30/300，奖励：-80.00, 步数：160\n",
      "回合：40/300，奖励：-57.00, 步数：114\n",
      "回合：50/300，奖励：-46.00, 步数：92\n",
      "回合：60/300，奖励：-66.00, 步数：132\n",
      "回合：70/300，奖励：-55.00, 步数：110\n",
      "回合：80/300，奖励：-28.00, 步数：56\n",
      "回合：90/300，奖励：-65.00, 步数：130\n",
      "回合：100/300，奖励：-27.00, 步数：54\n",
      "回合：110/300，奖励：-37.00, 步数：74\n",
      "回合：120/300，奖励：-27.00, 步数：54\n",
      "回合：130/300，奖励：-32.00, 步数：64\n",
      "回合：140/300，奖励：-46.00, 步数：92\n",
      "回合：150/300，奖励：-28.00, 步数：56\n",
      "回合：160/300，奖励：-29.00, 步数：58\n",
      "回合：170/300，奖励：-16.00, 步数：32\n",
      "回合：180/300，奖励：-24.00, 步数：48\n",
      "回合：190/300，奖励：-23.00, 步数：46\n",
      "回合：200/300，奖励：-20.00, 步数：40\n",
      "回合：210/300，奖励：-30.00, 步数：60\n",
      "回合：220/300，奖励：-18.00, 步数：36\n",
      "回合：230/300，奖励：-16.00, 步数：32\n",
      "回合：240/300，奖励：-17.00, 步数：34\n",
      "回合：250/300，奖励：-13.00, 步数：26\n",
      "回合：260/300，奖励：-14.00, 步数：28\n",
      "回合：270/300，奖励：-27.00, 步数：54\n",
      "回合：280/300，奖励：-20.00, 步数：40\n",
      "回合：290/300，奖励：-15.00, 步数：30\n",
      "回合：300/300，奖励：-17.00, 步数：34\n",
      "完成训练！用时：0.11 秒\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cfg = Config()\n",
    "set_seed(cfg.seed)\n",
    "env = create_env(cfg)\n",
    "policy = Policy(cfg)\n",
    "train_res = train(cfg, env, policy)\n",
    "plot_rewards(train_res['rewards'], title=f\"{cfg.algo_name} on {cfg.env_id} - Training\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开始测试\n",
    "\n",
    "训练完成后，为了评估智能体的性能，我们进行测试。在测试过程中，智能体使用训练好的动作价值函数来选择最优动作，即不包含探索机制。我们同样会与环境交互，并记录每一轮的奖励和步数，最后进行可视化展示。在复杂环境中，奖励的波动可能较大，而且有时曲线收敛后可能不也不一定代表策略最优（例如奖励设置不当的情况），因此需要渲染环境来直观观察智能体的行为表现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "状态数：48，动作数：4\n",
      "开始测试！\n",
      "回合：1/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "回合：2/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "回合：3/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "回合：4/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "回合：5/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "回合：6/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "回合：7/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "回合：8/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "回合：9/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "回合：10/10，奖励：-13.00, 步数：13, 动作序列：['Up', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Right', 'Down']\n",
      "完成测试！用时：0.00 秒\n"
     ]
    },
    {
     "data": {
      "image/png": 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fffcdcPdLLA/D448/jqOjI5s3b86w/YsvvrjjeUFBQaSlpfH5559n2L5+/XrruA/i5ZdfJikpicjIyEz7zp07x+LFi6lQoYJhgQqgcuXKeHp6snTpUhwcHHjiiScy7G/QoAE//vgjBw8etN6KAW5+EvDWrR5uBaoff/yR8+fP37WnRYsWpVGjRjz99NPMmDHjgT45FhQURGJiIocOHbJuu379eoYF9j4+Phn+fgYEBFj3hYSEUKRIEZYtW8ZPP/1kvfQH/wuVmzZtyvCYX3zxBeXKlctyfdJjjz2W4bEee+yxbD23W2sQL1y4kGG88+fPM2PGDJKSkti/fz/169fnhx9+wGQyUaVKFV577TUqVapknfW820zgvcjua0byDs1USZ4XEhLCyJEjeeedd/j555959tln8fHxoVu3bixduhSA9u3bZ/km3r59e1asWEGPHj149dVXKVGiBN999x0LFy6kS5cuODs7ExgYCMDrr79Ohw4dSE5OZuXKldaP6l+9evWR34SwdOnSdOjQgYiICFJTU6lcuTKbN2/mq6++ArJ+E2rUqBHBwcGMGzeOM2fOULlyZXbt2sXChQt59tlnrYvDs6tmzZoMGjSIyMhI4uPjeeaZZ/D09OTXX38lOjqaGzdu3DZwPQiTycQTTzzBhg0brJ/4+7uQkBDrLR6Cg4Ot2wMDA9m4cSOrVq3Cz8+Pw4cPM3fuXEwmU5brxv5pzJgxfPPNN0ycODHTbRLuVevWrVmwYAH9+vVj0KBBFC5cmCVLlvDXX39lmFHJiqOjI61atWLFihX4+PhkeI4A/fr1Y/To0Xh4eNC0aVO2bt3Kxo0bmT59erbqvR/+/v60bduW8ePHc+rUKapXr87x48eZPn26de1bWloa+fPnZ8SIEQwYMIAiRYrw3XffcejQIbp16wb8b8Zr8+bNNGrUKMsZtjvJ7mtG8g6FKhGgR48e1KxZk/fff5+3336bCxcuUKRIEZ555hlKly7NokWL+P3333nzzTcz/c+8QIECrFy5kvfee493332XS5cuUbJkSYYOHcrLL78M3HwjnjBhAkuWLOHzzz+nSJEiBAcHM2vWLPr168fevXutn1p6lMaPH0+BAgVYvHgxly9fpl69evTp04fZs2dnecuCW5+6mjlzJkuXLuX8+fOUKlWKIUOGWD8R9aD69OlD1apVrXdWT05OpkSJEoSGhlqDq9Hq1avHxo0bM6yn+vs+i8VCzZo1M1yCGjVqFKmpqURGRpKSkkKpUqXo06cPR48e5csvv7ynxdnFihVjyJAhvP7666xbt846M3M/nJyciI6O5s0332TSpEk4OTnRtm1bPDw87vmWB+3ateP999+ndevWmcJB+/btSUlJYfHixaxZs4bSpUszbdq0DHeaf5jeeust5s+fz4cffsjp06fx9vamZcuWDB48GEdHRxwdHVm8eDHvvfceb775JhcvXqRcuXK8/vrrtG/fHrj5Gqxfvz7vvfcecXFxLFiwIFu1ZOc1I3mHyaJvlxS5q99//53ly5fTv3//bK8NsTdJSUls27aNhg0bZlgHNW3aND755JMM358n9u3XX3/l2LFjtGjRIsP91p577jmKFy+eaYG6ZI9eM3I3mqkSuQe+vr7We9fkFq6urrz55ptUqVKF7t27U6BAAb7//ntWrFiRYaG22L+rV68yaNAgXnzxRf71r3+Rnp5ObGwsP/74I8OGDbN1ebmGXjNyN5qpEsnDDh06RGRkJN9//z3Xrl2jTJkyvPDCC3Tu3DnLO8yLffr888+Jjo4mPj4ei8VC1apV6dOnz20vZ0r26TUjd6JQJSIiImIAfVRBRERExAAKVSIiIiIGUKgSERERMYBClYiIiIgBdEsFA1ksFsxmrfu3Bw4OJvXCTqgX9kO9sB/qhf1wcDAZ9slNhSoDmUwmLl68Slrao/8eN/kfJycHPD0Lqhd2QL2wH+qF/VAv7IuXV0EcHY0JVbr8JyIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAFqoLiIieZ7ZbCY9Pe0RPZaJ69cdSUm5QXq6PgH4MDk6OuHg8OjmjxSqREQkz7JYLFy8eJ5r1y4/0sf9808HzGZ98u9RcHV1o3Bhr0fyhdcKVSIikmfdClRubp64uOR7JG+8AI6OJs1SPWQWi4WUlBtcvnwBAHd374f+mApVIiKSJ5nN6dZA5eZW+JE+tpOTg+5R9Qi4uOQD4PLlCxQq5PnQLwVqobqIiORJ6enpwP/eeCV3utXfR7FmTqFKRETytEd1yU9s41H2V6FKRERExAAKVSIiIiIGUKgSERHJY2JjPyMkpM49Hx8dPZ/nnmtj2OP/8cfvhITUYd++PYaNaQ/06T8REZE8plmzfxEcXO+ej//3v7vSvn2nh1hR7qBQJSIiksfky5effPny3/PxBQoUoECBAg+xotxBoUpERORvLBYLKakP9x5S6WZLlvepcnF2uO9PrF28mMzChfPYvn0bSUlJ+Pv788orfalduw7R0fPZv38v3t7exMV9x9NPt8LfvwpTp07m229vXn67cOECkZHvsHNnHI6OjrRu/QyHDv1EjRq16NmzN9HR89m48T98/PFn/PHH73Ts2JY33pjGypXLOHr0CN7eRejatQft2rUHICUlhYUL5/Lf/27l3LmzuLoWoE6dIIYMGYmnp+eD/fDsmEKViIjI/7NYLLy1Yh9HTyXbrIYKpdwZ3bn2PQer9PR0XnutP2lpqYwf/zoeHp58/PGHDBnSn7lzowH4/vt9dOz4b5YsWYnZbObgwQPW881mMyNGDCY9PZ3w8CicnZ2JiorgwIH91KhRK8vHnTkzgiFDRlC+vB8xMSt57723qVs3GF/fksyZM5Pt279hzJiJlCjhy9GjvzJ16mSWLVvMoEFDH+wHZMcUqkRERP4uh922ateuHfzyyyGWLfuQxx6rAMCwYaM5dOgnPvhgOeXKlQegZ8/euLm5AWQIVd9/v+//j/2YMmXKAfD662/x3HNt7/i4L7zQmZCQxgD06tWPTz5ZzU8/HcTXtyRVqlSlSZNm1lBWvHgJ6tYN4tixo4Y+d3ujUCUiIvL/TCYTozvXfuiX/+70NTX3e/nv2LGjuLm5WQMV3HweNWrUZteuOMqVK4+np5c1UP3TL78cplChwtZABeDl5U2ZMmXv+Lhly5a3/v7W2GlpN+9a/uSTLdm9eydz50Zx8uRv/PbbCX77LYHAwJr3/LxyIoUqERGRvzGZTORzcXyoj+Hk5ICjgzFTYhbL7b+Y2WIx4+R0820+X76sv4rH0dERi+X+Q6Szs3OWtbz77lS++morTz/dipCQRlSsGMaqVSs4e/bMfT9OTqJQJSIikoP5+VXk8uXLHDt21DpbZbFY+OGH762X/u6kQoWb5ycknKBs2XIAJCcnkZj4W7bqSU5O4tNPP2Hy5Kk0a9bCuv3EieO5/hOEuvmniIhIDhYU9AQVK1Zi8uRx7N+/lxMnjhMR8Q7x8Ufp2PHFu55fu3YdqlatzpQpE/jxx4P8+usRJk8ex/Xr17P1vXkFC7rh5ubGN998TWLiSeLjjzJt2pscOXKYlJSU7DzFHEOhSkREJAdzdHQkImI2FSv6M2bMcMLCunL8eDwzZsylevWAexpj6tR3KVq0GIMH92Hw4D5UrVodH5/it73EdzdOTk5MmfI2x4/H063bCwwdOoAbN67Tu3c/Tpw4zvXr1+97zJzCZMnqYqydmjBhAikpKbz99tsZtq9Zs4alS5dy8uRJihUrxnPPPUfPnj1xdLz7dfHjx4/Tvn17xo8fT/v27R+ovgsXrmS5+FAeDScnBzw9C6oXdkC9sB/qRWapqSn89dcfeHuXwNnZ5ZE+9p0Wqj9qSUlJ/PTTQYKD61nXYKWmptKyZTOGDh3JU0+1snGFD+ZuffbyKoijozFzTDlmpspsNhMREUFMTEymfevXr2fixIl06dKF9evXM3jwYObPn8/cuXPvOm5qairDhg3j6tWrD6NsERERu+bo6MjEiaOZOzeKxMSTHD9+jHffnYqLizNPPNHA1uXlKDlioXp8fDxjx44lISEBX1/fTPtXrVrFM888w/PPPw9AmTJlOH78OKtXr6Z///53HDsqKirLj5mKiIjkdoUKFeKddyJZuHAO69evxcHBREBADWbOnI+Hh4ety8tRckSo2rFjB35+fsyePZvBgwdn2j9s2DC8vLwybHNwcCA5+c53xN29ezcxMTGsW7eO0NBQQ2o1agpRsu9WD9QL21Mv7Id6kZnZbJu7fN5a+20ygb0swKlduw5z5y62dRkPlaOjCSenzH//s7EWP0s5IlR17tz5jvsff/zxDH++dOkSq1atomHDhlmec/HiRUaMGMG4ceMoUaKEIXUCFC7sathY8mDUC/uhXtgP9eJ/rl935M8/HbJ8s33YFHAfDbPZhIODA+7uBcif/96/RDo7bB6qEhMTadasWZb74+LiMs1C3cmVK1fo27cvN27cYMSIEVkeN2nSJGrVqkWbNm3uq967uXjxGunp9rH4MK9ydHSgcGFX9cIOqBf2Q73ILCXlBmazmfT0rL/c+GEwmW72Iz3dbDczVblZeroFs9lMcvJVrl1Lz7Tf3d0VBwdjAq7NQ5WPjw+xsbFZ7nd3d7/nsc6dO0fv3r1JTEwkOjqaUqVK3fa4devWsWfPHj777LP7rvdu0tPNdvOJjrxOvbAf6oX9UC/+Jz3dNonmVpBSoHq0sgrPRvbB5qHK2dkZPz+/Bx4nPj6esLAwzGYzK1eupGLFilkeu2bNGv76669M66gmTpxIbGwsixYteuB6REREJG+xeagywsmTJ+nevTuFCxcmOjr6rmukwsPDM918rEWLFgwcOJC2be/8rdwiIiIit5MrQtWYMWNISUkhIiICJycnzp07Z91XtGhRAM6fP4+zszOFChXCx8fntuN4e3tnuU9ERETkTnJ8qDpz5gy7du0CoF27dpn2//LLLwA899xzBAUFZboTu4iIiIgRctzX1Ng7fQWE7enrOOyHemE/1IvM9DU1D09aWhpr1sTw/PM3b4kUHT2fjRv/w8cfG/sBsXsZV19TIyIiIjnW5s2fExU13dZlPHIKVSIiImKovHoRLMevqRIRETGSxWKBtJSH/BgOWLK6/Ofkguk+vzslLm47ixbN48SJY7i6FqBevQYMGDCEo0eP8Npr/Xj99beZNy+KM2fOUL16AGPHTmLVquV8/vkGnJyc6djxBbp372kdb+PG//Dhhys5efI3vLy8aN26HV279sDR0RGAM2dOM3/+bPbs2cXVq1cIDKxJ376DqFChIrGxnzF16mQAQkLqMHPmPOu4K1YsZc2aj0hOTqZateqMGDGW0qXLAHD58mVmz57BN998RWpqKv7+VejbdyCVK1e1nv/pp5/wwQfLOHfuHHXrBlGiRObvA7YlhSoREZH/Z7FYuLr+TcxnjtqsBkefiri2HXPPwSopKYmxY4fTv/9r1K8fwtmzZ5gyZSJz5sygRYunSU9PZ9myxUyc+AZpaWkMHz6Yl156kdat27Fgwft88cVGFi6cS0hIY/z8KvDRRx8wb94s+vd/jbp1g/n55x+JiJhGcnIygwYN5erVK/Tp0xNf35K8/fZ7ODu7sHjxAvr3f4WlS1fRrNm/uHz5MjNnvsenn35O4cLu7N+/l9On/+DgwQO8++4MUlNTmDJlAm+/PYXZsxdisVgYPnwgLi75mTYtEjc3Nz7/fAN9+vRk/vwlVKpUmc2bPyciYhqDBg2jTp0gtm37igUL5lCsmP18al+X/0RERP7GhG2+aDm7zp07Q0pKCj4+xSlevASBgTWZNi2CDh2etx4TFvYqlStXpXr1QB5/vC6urq707TuQMmXK0rXrSwAcO3YUi8XCihXv0759J9q370jp0mV48smW9Oz5KmvXruby5cts2rSR5OQkpkyZRtWq1alYsRKTJr1Bvnz5+eSTj8iXLz9ubm4AeHsXwdnZGQAnJycmTJhChQoVqVKlGu3atefw4Z8B2Lt3Nz/+eJApU96iWrXqlC1bjt69+1GtWgCrV38IwMcfx9C8eQvat+9ImTJl6dLlJRo0yPo7fm1BM1UiIiL/z2Qy4dp2zEO//HfHT//d5+W/ihX9ad78SUaOfA1v7yLUrRtM/foNadQolB9++B6AUqVKW493dXWlRAlf62Pky3fzS4ZTU1NJSrrA+fN/ERhYM8Nj1KpVm7S0NBISThAff5TSpcvi6elp3Z8vX36qVq1GfHx8lnV6eXlTsKCb9c+FChXmxo0bABw5chiLxUKHDq0znJOSkmI95tixozRv/mSG/dWrB/Lrr0fu5cf0SChUiYiI/I3JZALnfA/3MZwcMJmMu6XCpElv8vLLr7Bjx3fs3r2TKVPGExhY07pOyskp49t9VqEtqwXmZrPlb+NkdYwZJyfHLGu805cWm81mChYsSHT0ikz7bs10gQmLJePP7J/Py9Z0+U9ERCQH++mnH5k58z3KlClHp04v8u67Mxg9egJ79+7mwoUL9zWWl5c3Xl7e1hmuWw4c2I+zszMlS5bCz68iJ08mcOHCeev+GzducPjwIcqVewzIOrRl5bHHKnDlyhVSU1MpVaq09dfKle/z7bdfA1CxYiV++OFAhvMOHz50X4/zsClUiYiI5GAFCxbkk09WM2fOTBITT3Ls2FG2bv2CUqXK4OHhcd/j/fvfXfnkk49Yu/ZjEhNP8sUXn7N48QLatn0WNzc3/vWvp3B392D8+FEcOvQTR4/+yuuvj+PatWu0a9ceuHmJEW6Gnhs3rt/p4QAIDq5HxYqVmDhxNPv27SEx8SRRURHExn5mDWpdurzEtm1f8cEHyzh58jc+/vhD/vvfrff9/B4m+5o3ExERkftSrlx53nzzXZYsWcjatatxcHCgdu26vPfeTM6cOX3f4/37311wcXEmJuYDZswIp1gxHzp37s6LL3YFwM3Njaio+cyaFcmgQX0BCAyswdy50fj6lgSgdu26VK1anT59Xmb8+Cl3fUxHR0emT5/DnDkzmDBhFNeuXaNcucd48813efzxugDUrx/CxIlvsHjxAhYtmke1agG88EIXNm/+/L6f48Oir6kxmL4Cwvb0dRz2Q72wH+pFZvqamrxBX1MjIiIiksMoVImIiIgYQKFKRERExAAKVSIiIiIGUKgSEZE8TZ/Xyt0eZX8VqkREJE9ydLx59++UlBs2rkQeplv9dXR8+HeR0n2qREQkT3JwcMTV1Y3Ll2/eddzFJd993wk8u8xmE+npmiF7mCwWCykpN7h8+QKurm53/JocoyhUiYhInlW4sBeANVg9Kg4ODpjNuk/Vo+Dq6mbt88OmUCUiInmWyWTC3d2bQoU8SU9PeySP6ehowt29AMnJVzVb9ZA5Ojo9khmqWxSqREQkz3NwcMDB4dHcVd3JyYH8+fNz7Vq67qqey2ihuoiIiIgBFKpEREREDKBQJSIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBlCoEhERETGAQpWIiIiIARSqRERERAyQ40LVhAkTGDVqVKbta9asoU2bNtSsWZMWLVqwYMEC0tPT7zjWunXraNmyJQEBAbRq1YqNGzc+rLJFREQkl8sxocpsNhMREUFMTEymfevXr2fixIl06dKF9evXM3jwYObPn8/cuXOzHO/TTz9l7NixdO7cmQ0bNtC6dWuGDBnC/v37H+bTEBERkVzKydYF3Iv4+HjGjh1LQkICvr6+mfavWrWKZ555hueffx6AMmXKcPz4cVavXk3//v0zHW+xWJgxYwbdunWjc+fOAPTp04c9e/awa9cuatWq9XCfkIiIiOQ6OSJU7dixAz8/P2bPns3gwYMz7R82bBheXl4Ztjk4OJCcnHzb8Y4fP86pU6do06ZNhu3R0dGG1SwiIiJ5S44IVbdmk7Ly+OOPZ/jzpUuXWLVqFQ0bNrzt8cePHwfg6tWr9OzZk59//plSpUrRp08fmjZt+kC1OjrmmCuqudatHqgXtqde2A/1wn6oF/bFZDJuLJuHqsTERJo1a5bl/ri4uEyzUHdy5coV+vbty40bNxgxYsRtj7l8+TIAI0eOpH///gwbNoxNmzbRt29flixZQr169e7vSfxN4cKu2T5XjKVe2A/1wn6oF/ZDvch9bB6qfHx8iI2NzXK/u7v7PY917tw5evfuTWJiItHR0ZQqVeq2xzk7OwPQs2dPnn32WQCqVKnCzz///MCh6uLFa6Snm7N9vjw4R0cHChd2VS/sgHphP9QL+6Fe2Bd3d1ccHIyZNbR5qHJ2dsbPz++Bx4mPjycsLAyz2czKlSupWLFilsf6+PgAUKlSpQzbK1SowH//+98HqiM93Uxaml4k9kC9sB/qhf1QL+yHemEfLBbjxsoVF3RPnjxJ9+7dcXV15cMPP7xjoAKoVq0aBQsW5MCBAxm2HzlyhDJlyjzMUkVERCSXsvlMlRHGjBlDSkoKERERODk5ce7cOeu+okWLAnD+/HmcnZ0pVKgQ+fPnJywsjNmzZ+Pj40NgYCAbNmxg+/btLF261EbPQkRERHKyHB+qzpw5w65duwBo165dpv2//PILAM899xxBQUG8/fbbAPTt2xdXV1emT5/OmTNn8PPzIyoqiuDg4EdXvIiIiOQaJovFyKuJcuHCFV0jtzEnJwc8PQuqF3ZAvbAf6oX9UC/si5dXQcNub5Er1lSJiIiI2JpClYiIiIgBFKpEREREDKBQJSIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBlCoEhERETGAQpWIiIiIARSqRERERAygUCUiIiJiAIUqEREREQMoVImIiIgYQKFKRERExAAKVSIiIiIGUKgSERERMYBClYiIiIgBFKpEREREDKBQJSIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBshxoWrChAmMGjUq0/Y1a9bQpk0batasSYsWLViwYAHp6elZjpOWlsaMGTNo0qQJtWrVonPnznz//fcPsXIRERHJzXJMqDKbzURERBATE5Np3/r165k4cSJdunRh/fr1DB48mPnz5zN37twsx5s7dy6rV69mypQprFu3jvLlyxMWFsbZs2cf5tMQERGRXCpHhKr4+HhefPFFVq9eja+vb6b9q1at4plnnuH555+nTJkytGzZkpdffpmPP/44yzG3bNlC69atCQkJoWzZsowaNYpLly5ptkpERESyJUeEqh07duDn58d//vMfSpUqlWn/sGHD6NmzZ4ZtDg4OJCcnZzmmt7c3X331FYmJiaSnpxMTE4OLiwuVK1c2vH4RERHJ/ZxsXcC96Ny58x33P/744xn+fOnSJVatWkXDhg2zPGfs2LEMGjSIZs2a4ejoiIODA1FRUZQpU+aBanV0zBE5NVe71QP1wvbUC/uhXtgP9cK+mEzGjWXzUJWYmEizZs2y3B8XF4eXl9c9j3flyhX69u3LjRs3GDFiRJbHHT16lEKFCjF79mx8fHxYvXo1w4YNY8WKFVSpUuW+nsPfFS7smu1zxVjqhf1QL+yHemE/1Ivcx+ahysfHh9jY2Cz3u7u73/NY586do3fv3iQmJhIdHX3bS4UAf/zxB0OHDmXp0qXUqVMHgICAAI4ePUpUVBRz5sy5vyfxNxcvXiM93Zzt8+XBOTo6ULiwq3phB9QL+6Fe2A/1wr64u7vi4GDMrKHNQ5WzszN+fn4PPE58fDxhYWGYzWZWrlxJxYoVszz2wIEDpKamEhAQkGF7jRo12LZt2wPVkZ5uJi1NLxJ7oF7YD/XCfqgX9kO9sA8Wi3Fj5YoLuidPnqR79+64urry4Ycf3jFQARQvXhyAX375JcP2I0eOUK5cuYdVpoiIiORiuSJUjRkzhpSUFCIiInBycuLcuXPWX7ecP3+eS5cuARAYGMjjjz/OyJEj2bFjBydOnCAyMpK4uDh69eplq6chIiIiOZjNL/89qDNnzrBr1y4A2rVrl2n/rdmo5557jqCgIN5++20cHByYO3cukZGRjB49muTkZCpVqsTSpUupUaPGI61fREREcgeTxWLk1US5cOGKrpHbmJOTA56eBdULO6Be2A/1wn6oF/bFy6ugYbe3yBWX/0RERERsTaFKRERExAAKVSIiIiIGUKgSERERMYBClYiIiIgBFKpEREREDKBQJSIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBnDK7oknT54kJSUFPz8/Ll26RGRkJKdOneKpp57imWeeMbBEEREREfuXrZmqr7/+mqeffpqPP/4YgAkTJvDhhx9y5swZRo8ezerVqw0tUkRERMTeZStUzZ07l5CQEPr168fFixfZvHkzvXr1Yu3atfTq1Ytly5YZXaeIiIiIXctWqDp8+DDdu3fHzc2Nbdu2kZ6ezpNPPglAgwYNSEhIMLRIEREREXuXrVCVL18+0tLSAPj222/x9vamcuXKAPz5558ULlzYuApFREREcoBsLVSvXbs2ixcv5uLFi2zatIlnn30WgB9//JFZs2ZRu3ZtQ4sUERERsXfZmqkaM2YMp0+fZujQoZQsWZI+ffoA0Lt3b1JSUhg2bJihRYqIiIjYu2zNVJUuXZrY2Fj++usvihQpYt0+e/ZsqlatiouLi2EFioiIiOQE2b5PlclkyhCoAGrWrPmg9YiIiIjkSPccqpo2bYrJZLrngbdu3ZqtgkRERERyonsOVUFBQdZQZTab2bBhA4UKFaJx48YULVqUpKQktm/fzvnz53n++ecfWsEiIiIi9uieQ9Xbb79t/X14eDiBgYFER0fj6upq3Z6amkqfPn24evWqsVWKiIiI2Llsffpv9erVvPLKKxkCFYCzszNdu3YlNjbWkOJEREREcopshSqA5OTk227//fffyZcvX7YLEhEREcmJshWqmjZtSnh4ONu3b7dus1gsbN68mcjISFq2bGlYgSIiIiI5QbZuqTB69GiOHj1Kz549cXFxwd3dnQsXLpCenk6DBg0YPny40XWKiIiI2LVsharChQvz0Ucf8fXXX7N3716Sk5Px9PTkiSeeoF69ekbXKCIiImL3shWqevbsSVhYGKGhoYSGhhpckoiIiEjOk601Vfv27buvG4GKiIiI5HbZClUNGzZk/fr1pKamGl2PiIiISI6Urct/+fLlY/369WzcuBE/Pz8KFCiQYb/JZOL99983pEARERGRnCBbM1WnT5+mVq1aVK9eHVdXVywWS4ZfZrPZ6DqtJkyYwKhRozJtX758OS1atCAgIIBWrVqxZs2au461cuVKmjVrRmBgIC+++CI///zzwyhZRERE8oBszVQtX77c6Druymw2ExkZSUxMDM8++2yGfTExMYSHh/PGG29Qs2ZN4uLiGD9+PO7u7jRv3vy2461du5Z33nmHKVOmULVqVRYsWECPHj3YuHEjXl5ej+IpiYiISC6SrVB1J1evXmXPnj00atTIsDHj4+MZO3YsCQkJ+Pr6Ztp/6dIlhg4dSps2bQAoXbo0H3zwAdu3b88yVM2bN48uXbrQtm1bAKZOnUrz5s1ZvXo1vXv3zladZrOZ61eukpb28Gbq5O6cnBy46mTh+pVr6oWNqRf2Q72wH7mpFy6u+XFwyPaXs+Q62QpVp06dYtKkSezatYuUlJTbHnPo0KEHKuzvduzYgZ+fH7Nnz2bw4MGZ9oeFhVl/n5qayubNm4mPj6d///63He+vv/7ixIkTGe6p5eTkRJ06ddi9e3e2Q1V68jmuvd83W+eKcVKBa7YuQgD1wp6oF/YjN/UiwaEEVXq9laODlZE3M8hWqHrrrbfYt28fHTt2ZN++fbi6ulKzZk22b9/OkSNHiIqKMq5CoHPnzvd03J49e+jatStms5kOHTrQrFmz2x53+vRpAEqUKJFhe7FixTh8+PCDFSsiIpJHmEwmPD0L5uhQZaRshardu3fz2muv0aVLF1asWMGXX37J8OHDGTJkCC+//DJbt27NMtD8U2Ji4h2PjYuLu+c1TuXLl2ft2rUcPHiQqVOn4unpeduvzLl27eb/EVxcXDJsz5cvHzdu3Linx7odR/eiFHx5PuZ0S7bHkAfn4GiikFt+Ll2+rl7YmHphP9QL+5GbelHZNT/JyTl73s3d3dWwUJitUHXlyhX8/f0BeOyxx5g1axYAjo6OvPjii0ybNu2ex/Lx8SE2NjbL/e7u7vc8lre3N97e3lSuXJnz588za9YsBg0alCk85c+fHyDTpcsbN27g6up6z4/3Tw4ODrjkd83x18hzOicnBwoUKsiNNJN6YWPqhf1QL+xHbuqF2cxD/cT/o2AxMNdmK1QVK1aMP//8E4CyZcuSnJzMuXPnKFq0KB4eHvz111/3PJazszN+fn7ZKcNq27Zt+Pr6UqFCBes2f39/UlJSSEpKolixYhmOv3XZ7+zZsxke++zZs/j4+DxQLSIiIpI3ZWu+q3HjxkRGRrJ//35KlixJ8eLFWbx4MZcvX2bNmjWPPJhERkYyZ86cDNsOHDiAh4cHRYoUyXS8t7c35cuXZ+fOndZtaWlp7Nmzh7p16z70ekVERCT3yVaoGjhwIIULF2bGjBkAvPbaa7z//vvUrVuXzz77jB49ehha5N2EhYURGxvLihUrSEhI4KOPPiI6OpoBAwZYr5MmJSWRlJRkPefll19myZIlrF27lqNHjzJmzBiuX7/Oc88990hrFxERkdwhW5f/PD09Wb16NWfPngWgbdu2+Pr68v333xMYGEhQUJChRd5Ny5YtSU1NZeHChUybNg1fX1/Gjx9Px44drccMGDAA+N+NSzt16sSlS5eIjIwkKSmJ6tWrs2TJEt34U0RERLLFZLHc/xKtiRMn0rRpU+rVq5dpEXhed+HClRy/8DCnc3JywNOzoHphB9QL+6Fe2A/1wr54eRXE0dGGn/77/vvviYmJwdXVleDgYJo1a0bjxo0zLQgXERERySuyNVMFcO7cObZt28Y333xDXFwcFy9epEqVKjRp0oTQ0FACAgKMrjVH0P88bE//C7Qf6oX9UC/sh3phX4ycqcp2qPo7s9nMrl27iIqKYu/evZhMJkO/piYn0YvE9vQPlv1QL+yHemE/1Av7YvPLf3DzxpkHDhxg165d7N69mwMHDnDt2jX8/PwIDg42pDgRERGRnCJboapLly4cPHiQlJQUypYtS1BQEB07diQ4OPi294USERERye2yFaoOHjzIjRs3qFatGk899RTBwcFUr15dX6goIiIieVa2v1D5+++/Jy4ujq1btzJjxgzy5ctH7dq1CQoKIjg4mMDAQKNrFREREbFbhixUv3LlCnv27GH16tVs2bJFC9W18NCmtAjUfqgX9kO9sB/qhX2xi4XqAH/++SffffcdcXFxxMXFcfr0aXx9fWncuLEhxYmIiIjkFNkKVVOnTiUuLo6jR4/i4OBArVq16Ny5M6GhoVSsWNHoGkVERETsXrZC1WeffUbDhg3p06cPISEhFC5c2Oi6RERERHKUbIWq7777DpPJBMClS5eIj4+ndOnSODo64ujoaGiBIiIiIjlBtlZmmUwmdu7cSceOHQkKCqJNmzb8+uuvDB06lLffftvoGkVERETsXrZCVVxcHD179iR//vwMGzaMWx8grFy5MsuWLWPJkiWGFikiIiJi77IVqiIjI2nWrBnLly+ne/fu1lD16quvEhYWxurVqw0tUkRERMTeZStUHTp0iA4dOgBY11bd0qBBA06dOvXglYmIiIjkINkKVYUKFeLcuXO33ffHH39QqFChBypKREREJKfJVqhq1qwZ06dP5+DBg9ZtJpOJ06dPM2/ePEJDQ42qT0RERCRHyNYtFYYOHcqBAwfo1KkTRYoUAWDIkCGcPn2aEiVKMGTIEEOLFBEREbF32QpV4eHhTJ48mSNHjrBjxw6SkpIoVKgQXbt2pX379ri6uhpdp4iIiIhdy1aoWr9+PU8//TSdOnWiU6dORtckIiIikuNka01VrVq12LFjh9G1iIiIiORY2Zqp8vf3Z/HixWzatInKlStToECBDPtNJhNTp041pEARERGRnCBboWrz5s0UK1aM1NTUDJ8AvOWf964SERERye2yFaq+/PJLo+sQERERydGytaZKRERERDJSqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBlCoEhERETGAQpWIiIiIARSqRERERAygUCUiIiJiAIUqEREREQPkuFA1YcIERo0alWn78uXLadGiBQEBAbRq1Yo1a9bccZzr16/z3nvv0bRpU2rVqkX79u3ZunXrwypbREREcrkcE6rMZjMRERHExMRk2hcTE0N4eDgDBgwgNjaW7t27M378eLZs2ZLleG+88QafffYZEydOZN26dTRv3pz+/fuzc+fOh/k0REREJJfKEaEqPj6eF198kdWrV+Pr65tp/6VLlxg6dCht2rShdOnSdOrUiUqVKrF9+/bbjnft2jXWrVvHkCFDaNy4MWXLlqVv374EBQXddYZLRERE5HZyRKjasWMHfn5+/Oc//6FUqVKZ9oeFhdGtWzcAUlNTiY2NJT4+ngYNGtx2PJPJxLx582jUqFGG7Q4ODly8eNH4JyAiIiK5npOtC7gXnTt3vqfj9uzZQ9euXTGbzXTo0IFmzZrd9rj8+fMTEhKSYdsPP/zAjh07GDdu3APV6uiYI3JqrnarB+qF7akX9kO9sB/qhX0xmYwby+ahKjExMcvwAxAXF4eXl9c9jVW+fHnWrl3LwYMHmTp1Kp6engwfPvyu5x07dox+/foRGBhIp06d7rn22ylc2PWBzhfjqBf2Q72wH+qF/VAvch+bhyofHx9iY2Oz3O/u7n7PY3l7e+Pt7U3lypU5f/48s2bNYtCgQbi4uGR5zr59++jbty/Fixdn3rx5ODs731f9/3Tx4jXS080PNIY8GEdHBwoXdlUv7IB6YT/UC/uhXtgXd3dXHByMmTW0eahydnbGz8/vgcbYtm0bvr6+VKhQwbrN39+flJQUkpKSKFas2G3P++KLLxg2bBg1atRgzpw5FCpU6IHqAEhPN5OWpheJPVAv7Id6YT/UC/uhXtgHi8W4sXLFBd3IyEjmzJmTYduBAwfw8PCgSJEitz3nyy+/5LXXXiM0NJTo6GhDApWIiIjkXTafqTJCWFgYQ4YMoXbt2jRs2JCdO3cSHR3NiBEjrFN6SUlJAHh4eJCcnMzIkSOpVq0aY8eOJTk52TqWs7MzHh4eNngWIiIikpPlilDVsmVLUlNTWbhwIdOmTcPX15fx48fTsWNH6zEDBgwAbt55fdu2bVy8eJEDBw5kuq1CUFAQy5cvf6T1i4iISM5nsliMvJooFy5c0TVyG3NycsDTs6B6YQfUC/uhXtgP9cK+eHkVNOz2FrliTZWIiIiIrSlUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBlCoEhERETGAQpWIiIiIARSqRERERAygUCUiIiJiAIUqEREREQMoVImIiIgYQKFKRERExAAKVSIiIiIGUKgSERERMYBClYiIiIgBFKpEREREDKBQJSIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBlCoEhERETGAQpWIiIiIARSqRERERAygUCUiIiJigBwXqiZMmMCoUaMybV++fDktWrQgICCAVq1asWbNmnse8/z584SEhBAVFWVkqSIiIpKH5JhQZTabiYiIICYmJtO+mJgYwsPDGTBgALGxsXTv3p3x48ezZcuWexp73LhxnDt3zuiSRUREJA/JEaEqPj6eF198kdWrV+Pr65tp/6VLlxg6dCht2rShdOnSdOrUiUqVKrF9+/a7jh0TE8OJEycoWrTowyhdRERE8ogcEap27NiBn58f//nPfyhVqlSm/WFhYXTr1g2A1NRUYmNjiY+Pp0GDBncc9/jx44SHh/Puu+/i4uLyUGoXERGRvMHJ1gXci86dO9/TcXv27KFr166YzWY6dOhAs2bNsjw2NTWVoUOH0rNnT6pVq2ZUqTg65oicmqvd6oF6YXvqhf1QL+yHemFfTCbjxrJ5qEpMTLxj+ImLi8PLy+uexipfvjxr167l4MGDTJ06FU9PT4YPH37bY2fOnEm+fPl45ZVXslV3VgoXdjV0PMk+9cJ+qBf2Q72wH+pF7mPzUOXj40NsbGyW+93d3e95LG9vb7y9valcuTLnz59n1qxZDBo0KNOlvV27drFq1SrWrl2Lo6Njtmu/nYsXr5GebjZ0TLk/jo4OFC7sql7YAfXCfqgX9kO9sC/u7q44OBgza2jzUOXs7Iyfn98DjbFt2zZ8fX2pUKGCdZu/vz8pKSkkJSVRrFixDMevXbuWq1ev0rZtW+u2a9euMX/+fD7//HM2bNiQ7VrS082kpelFYg/UC/uhXtgP9cJ+qBf2wWIxbiybhyojREZGUq5cOSIiIqzbDhw4gIeHB0WKFMl0/LBhw3j11VczbOvatSstWrSgR48eD71eERERyX1yRagKCwtjyJAh1K5dm4YNG7Jz506io6MZMWKEdUovKSkJAA8PD+tlwr9zcnLC3d2dkiVLPuryRUREJBfIFaGqZcuWpKamsnDhQqZNm4avry/jx4+nY8eO1mMGDBgA3LzzuoiIiIjRTBaLkVcT5cKFK7pGbmNOTg54ehZUL+yAemE/1Av7oV7YFy+vgobd3kI3yRARERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBlCoEhERETGAQpWIiIiIARSqRERERAygUCUiIiJiAIUqEREREQMoVImIiIgYQKFKRERExAAKVSIiIiIGUKgSERERMYBClYiIiIgBFKpEREREDKBQJSIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBlCoEhERETGAQpWIiIiIARSqRERERAygUCUiIiJiAIUqEREREQMoVImIiIgYQKFKRERExAAKVSIiIiIGyFGhasKECYwaNSrT9uXLl9OiRQsCAgJo1aoVa9asuetYX3/9Ne3btycgIIDmzZuzcuXKh1GyiIiI5BE5IlSZzWYiIiKIiYnJtC8mJobw8HAGDBhAbGws3bt3Z/z48WzZsiXL8Xbt2kWfPn0IDQ1lw4YN9O7dmzfffJPY2NiH+TREREQkF3OydQF3Ex8fz9ixY0lISMDX1zfT/kuXLjF06FDatGkDQOnSpfnggw/Yvn07zZs3v+2YUVFRNG/enIEDBwJQpkwZ9u/fz549e2jZsuXDezIiIiKSa9l9qNqxYwd+fn7Mnj2bwYMHZ9ofFhZm/X1qaiqbN28mPj6e/v3733a8a9eusWfPHmbOnJlh+9SpUw2tW0RERPIWuw9VnTt3vqfj9uzZQ9euXTGbzXTo0IFmzZrd9riEhATMZjOOjo4MHDiQ3bt3U6xYMbp06ULHjh0fuF5HxxxxRTVXu9UD9cL21Av7oV7YD/XCvphMxo1l01CVmJiYZfgBiIuLw8vL657GKl++PGvXruXgwYNMnToVT09Phg8fnum4y5cvAzcXvffq1Ys+ffqwc+dOJk+eDPDAwapwYdcHOl+Mo17YD/XCfqgX9kO9yH1sGqp8fHzuuDjc3d39nsfy9vbG29ubypUrc/78eWbNmsWgQYNwcXHJcJyzszMA7dq1o1u3bgBUqVKFhIQEli5d+sCh6uLFa6Snmx9oDHkwjo4OFC7sql7YAfXCfqgX9kO9sC/u7q44OBgza2jTUOXs7Iyfn98DjbFt2zZ8fX2pUKGCdZu/vz8pKSkkJSVRrFixDMcXL14cgEqVKmXYXqFCBT755JMHqgUgPd1MWppeJPZAvbAf6oX9UC/sh3phHywW48bK8Rd0IyMjmTNnToZtBw4cwMPDgyJFimQ63sfHhzJlynDgwIEM248cOUKZMmUeaq0iIiKSe+X4UBUWFkZsbCwrVqwgISGBjz76iOjoaAYMGGCdzktKSiIpKcl6Tv/+/YmJiWHlypWcPHmSDz/8kDVr1tCzZ08bPQsRERHJ6ez+039307JlS1JTU1m4cCHTpk3D19eX8ePHZ1gbNWDAAODmndfh5noqgPnz5/PWW29RsmRJJk6cyDPPPPPI6xcREZHcwWSxGHk1US5cuKJr5Dbm5OSAp2dB9cIOqBf2Q72wH+qFffHyKmjY7S1y/OU/EREREXugUCUiIiJiAIUqEREREQMoVImIiIgYQKFKRERExAAKVSIiIiIGUKgSERERMYBClYiIiIgBFKpEREREDKBQJSIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAJovFYrF1EblJerrZ1iUI4OjooF7YCfXCfqgX9kO9sB8ODiZMJpMhYylUiYiIiBhAl/9EREREDKBQJSIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUPyGw2M3PmTBo2bEjNmjV55ZVXOHnypK3LypOSkpKYMGECjRo1onbt2vz73/9mz549ti4rzzt+/Di1atXik08+sXUpeda6deto2bIlAQEBtGrVio0bN9q6pDwrLS2NGTNm0KRJE2rVqkXnzp35/vvvbV1WnjN//ny6du2aYduhQ4fo0qULNWvWpGnTpixbtuy+x1WoekBz5szhgw8+YMqUKXz44YeYzWbCwsJISUmxdWl5zpAhQ9i/fz8RERGsWbOGKlWq0LNnT44dO2br0vKs1NRUhg0bxtWrV21dSp716aefMnbsWDp37syGDRto3bq19bUij97cuXNZvXo1U6ZMYd26dZQvX56wsDDOnj1r69LyjJUrVxIZGZlh24ULF+jRowdlypRhzZo19OvXj/DwcNasWXNfYytUPYCUlBQWL17MwIEDCQ0NpXLlykyfPp3Tp0/zxRdf2Lq8PCUhIYHt27czadIk6tSpQ/ny5Rk/fjzFihXjs88+s3V5eVZUVBRubm62LiPPslgszJgxg27dutG5c2fKlClDnz59qF+/Prt27bJ1eXnSli1baN26NSEhIZQtW5ZRo0Zx6dIlzVY9AmfOnOHVV18lPDyccuXKZdj30Ucf4ezszOuvv46fnx8dOnTgpZdeYsGCBff1GApVD+Dw4cNcuXKFevXqWbcVLlyYqlWrsnv3bhtWlvd4enqyYMECAgICrNtMJhMmk4mLFy/asLK8a/fu3cTExPD222/bupQ86/jx45w6dYo2bdpk2B4dHU3v3r1tVFXe5u3tzVdffUViYiLp6enExMTg4uJC5cqVbV1arvfTTz/h7OzM+vXrqVGjRoZ9e/bsISgoCCcnJ+u2J554ghMnTvDnn3/e82MoVD2A06dPA1CiRIkM24sVK2bdJ49G4cKFady4MS4uLtZtmzZtIiEhgYYNG9qwsrzp4sWLjBgxgnHjxmV6fcijc/z4cQCuXr1Kz549qVevHh07duTLL7+0cWV519ixY3F2dqZZs2YEBAQwffp0Zs6cSZkyZWxdWq7XtGlToqKiKF26dKZ9p0+fpnjx4hm2FStWDIA//vjjnh9DoeoBXLt2DSDDGzlAvnz5uHHjhi1Kkv+3b98+Ro8eTYsWLQgNDbV1OXnOpEmTqFWrVqYZEnm0Ll++DMDIkSNp3bo1ixcvpkGDBvTt25e4uDgbV5c3HT16lEKFCjF79mxiYmJo3749w4YN49ChQ7YuLU+7fv36bd/Lgft6P3e6+yGSlfz58wM311bd+j3cbICrq6utysrztmzZwrBhw6hduzbh4eG2LifPWbduHXv27NFaNjvg7OwMQM+ePXn22WcBqFKlCj///DNLlizJsHRBHr4//viDoUOHsnTpUurUqQNAQEAAR48eJSoqijlz5ti4wrwrf/78mT5gditMFShQ4J7H0UzVA7h1WeOfn9o4e/YsPj4+tigpz1uxYgUDBgygSZMmzJs3z/o/DXl01qxZw19//UVoaCi1atWiVq1aAEycOJGwsDAbV5e33Pp3qFKlShm2V6hQgcTERFuUlKcdOHCA1NTUDGs/AWrUqEFCQoKNqhKA4sWL3/a9HLiv93PNVD2AypUr4+bmxs6dO63Xwy9evMjPP/9Mly5dbFxd3nPr1hZdu3Zl7NixmEwmW5eUJ4WHh3P9+vUM21q0aMHAgQNp27atjarKm6pVq0bBggU5cOCAdWYE4MiRI1rDYwO31uz88ssvBAYGWrcfOXIk06fR5NGqW7cuH374Ienp6Tg6OgKwY8cOypcvj7e39z2Po1D1AFxcXOjSpQvh4eF4eXlRsmRJ3n33XYoXL06LFi1sXV6ecvz4caZOncq//vUvevfuneHTGvnz56dQoUI2rC5vyep/dd7e3prBfcTy589PWFgYs2fPxsfHh8DAQDZs2MD27dtZunSprcvLcwIDA3n88ccZOXIkEydOpHjx4qxbt464uDhWrVpl6/LytA4dOrBo0SLGjh1LWFgYP/zwA0uXLmXy5Mn3NY5C1QMaOHAgaWlpjBs3juvXr1O3bl2io6Otaxnk0di0aROpqals3ryZzZs3Z9j37LPP6mP9kmf17dsXV1dXpk+fzpkzZ/Dz8yMqKorg4GBbl5bnODg4MHfuXCIjIxk9ejTJyclUqlSJpUuXZvqIvzxa3t7eLFq0iDfffJNnn32WokWLMmLECOtaxHtlslgslodUo4iIiEieoYXqIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGEChSkRERMQAClUikmd88skn+Pv7P5IvEx41ahRNmzZ96I8jIvZDoUpE8ozQ0FBiYmIoVqyYrUsRkVxI3/0nInmGl5cXXl5eti5DRHIpzVSJSI6xevVqWrVqRfXq1QkNDSUqKor09HTg5uW2rl278vHHH9OkSRNq1apF9+7dOXz4sPX8f17+O3/+PEOHDqVBgwYEBATQrl071q1bl+ExT5w4wcCBA2nQoAE1a9aka9eu7N27N8MxycnJjB49mqCgIOrWrcu7776L2WzOVP+WLVto3749AQEBNGjQgDfeeIOrV69a91+/fp1JkybRqFEjqlevzlNPPUV0dLRRPz4Recg0UyUiOcL8+fOZPn06Xbp0YfTo0Rw6dIioqCj++OMPpk6dCsChQ4c4duwYQ4YMwd3dnZkzZ9KlSxdiY2Nve8lv+PDh/PXXX0yePBk3Nzc+/fRTRo4cSfHixXniiSc4evQonTp1oly5cowbNw5nZ2eWLVtG9+7dWbx4MUFBQZjNZsLCwjh16hQjR47Ew8ODRYsWcfDgwQyP+dlnnzFs2DDatGnD4MGDOXXqFNOnT+fo0aMsWbIEk8nE1KlT+fbbbxk5ciRFihRh27ZtvPPOO3h4eNChQ4dH9rMWkexRqBIRu3fp0iXmzJnD888/z7hx4wAICQnBw8ODcePG0aNHD+tx8+bNo06dOgAEBgbSvHlzli1bxrBhwzKNu2vXLvr160fz5s0BCAoKwsPDAxcXFwBmzZqFi4sLy5Ytw83NDbi5Lqt169a88847fPzxx2zbto0ffviBhQsX0qhRIwDq1auXYZG6xWIhPDychg0bEh4ebt1erlw5XnrpJb7++mtCQ0PZtWsXDRo0oFWrVgAEBwdToEABvL29Df15isjDoVAlInZv//79XL9+naZNm5KWlmbdfiu4bN++HYBSpUpZAxVAsWLFqFWrFrt3777tuMHBwURFRfHzzz/TsGFDGjduzMiRI637d+3aRZMmTayBCsDJyYlWrVoxe/Zsrly5wp49e3B2dqZhw4bWYwoUKEDjxo2tj3vs2DFOnz5N7969M9Rft25d3Nzc2L59O6GhoQQHB/Phhx9y+vRpGjduTOPGjenXr9+D/OhE5BFSqBIRu5eUlARAr169brv/7NmzAPj4+GTa5+3tzU8//XTb86ZPn868efPYuHEjmzZtwsHBgfr16/P6669TsmRJkpOTKVKkSKbzihQpgsVi4fLlyyQnJ+Ph4YHJZMpwTNGiRTPVP3nyZCZPnpxl/WPHjqV48eKsX7+eKVOmMGXKFGrVqsWkSZOoXLnybZ+DiNgPhSoRsXuFCxcGIDw8nHLlymXaX6RIEWbMmMGFCxcy7fvzzz+zvHxWqFAhhg8fzvDhwzl27Bhbt25lzpw5TJ48mQULFuDu7s6ff/6Z6bxz584B4OnpiaenJxcuXCA9PR1HR0frMbeC1N/rHzFiBEFBQZnGc3d3B8DFxYU+ffrQp08ffv/9d7766ivmzJnD0KFD2bBhQxY/HRGxF/r0n4jYvRo1auDs7MyZM2cICAiw/nJyciIiIsL6ab4TJ04QHx9vPe/MmTPs37+fevXqZRrz1KlTNG7cmM8//xyAxx57jFdeeYX69evz+++/Azcvz3311VdcvnzZel56ejobNmwgICAAFxcX6tWrR1paGlu2bLEek5KSYr0keWtsb29vEhMTM9Tv4+PDe++9x88//8z169d58sknWbx4MQC+vr507tyZVq1aWesREfummSoRsXuenp6EhYUxY8YMLl++THBwMGfOnGHGjBmYTCbrpTGLxcKrr77Ka6+9hqOjI7NmzcLd3Z2uXbtmGrNkyZIUL16cN954g8uXL1OmTBl+/PFHvv76a3r37g1A//792bZtG926daNXr144OzuzYsUKTp48yaJFi4Cbi9JDQkIYN24cf/31FyVLlmTZsmWcP3/eOkPm6OjIa6+9xoQJE3B0dKRJkyZcvHiROXPmcObMGapVq0b+/PmpVq0as2bNwtnZGX9/f44fP87atWt58sknH9FPWkQehMlisVhsXYSIyL1YuXIlH3zwAQkJCbi7u1OvXj2GDBmCr68vo0aNYteuXbzyyivMnj2ba9euUb9+fUaOHEmpUqWAm/epGj16NFu3bqVUqVKcO3eOiIgIvv32Wy5cuECJEiXo0KEDvXr1wsHh5kT+oUOHiIiIYM+ePZhMJgIDA+nfv3+GBfHXrl0jPDycDRs2cOPGDVq2bEmBAgXYunUrX375pfW42NhYFi1axK+//kqBAgWoXbs2gwcPxt/fH4DLly8TGRnJ1q1bOXfuHN7e3rRs2ZJBgwaRP3/+R/iTFpHsUKgSkVzhVqj6e4gREXmUtKZKRERExAAKVSIiIiIG0OU/EREREQNopkpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhEREREDKFSJiIiIGOD/AJut/V/kx6IAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cfg.max_episode = 10 # 测试时只跑10个回合\n",
    "# cfg.render_mode = 'human' # 测试时渲染环境, 不要在Notebook中开启渲染，会卡死\n",
    "env_test = create_env(cfg)\n",
    "test_res = test(cfg, env_test, policy)\n",
    "plot_rewards(test_res['rewards'], title=f\"{cfg.algo_name} on {cfg.env_id} - Testing\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "joyrl-book",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
